L3-CDH-AUTO-LEARN
ESSENTIAL
COMMAND CONTROL
Level 3 · software
Machine Learning Adaptation Engine
Online Learning and Model Adaptation System
ML system enabling Ark to learn from operational experience and adapt over 100 years
Purpose
ML system enabling Ark to learn from operational experience and adapt over 100 years
Context
Child of L2-CDH-AUTO
Principles
- ▸Online learning updates models incrementally without retraining from scratch
- ▸Transfer learning applies lessons from one subsystem to analogous systems
- ▸Anomaly detection learns normal behavior patterns and flags deviations
- ▸Conservative learning bounds prevent unsafe model updates
Typical implementations
- ▸JPL AEGIS autonomous science targeting with online learning
- ▸NASA ISHM online model adaptation for fault detection
- ▸Incremental ML frameworks (River, scikit-multiflow)
Lunar considerations
- ▸Novel lunar environment means initial models incomplete - learning is essential
- ▸Learning must be bounded to prevent drift toward unsafe operational modes
- ▸Computational resources for learning limited by power budget
Specifications
Functional
| primary function | ML system enabling Ark to learn from operational experience and adapt over 100 years |
Interfaces
Provides
- Updated models improving planning effectiveness
Requires
- Sensor data for model training and validation
Cite this entry
Lunar Ark Codex. "Machine Learning Adaptation Engine" (L3-CDH-AUTO-LEARN). Retrieved 10 September 2026, from https://lunarark.com/entry/L3-CDH-AUTO-LEARN
Licensed CC-BY-SA 4.0. You may reuse and adapt this entry with attribution, under the same licence.